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We report an investigation of the effect of substitution of Y for Tm in $Tm_{1-x}Y_xVO4$ via low-temperature heat capacity measurements, with the yttrium content $x$ varying from $0$ to $0.997$. Because the Tm ions support a local quadrupolar (nematic) moment, they act as reporters of the local strain state in the material, with the splitting of the ion's non-Kramers crystal field groundstate proportional to the quadrature sum of the in-plane tetragonal symmetry-breaking transverse and longitudinal strains experienced by each ion individually. Analysis of the heat capacity therefore provides detailed insights into the distribution of local strains that arise as a consequence of the chemical substitution. These local strains suppress long-range quadrupole order for $x>0.22$, and result in a broad Schottky-like feature for higher concentrations. Heat capacity data are compared to expectations for a distribution of uncorrelated (random) strains. For dilute Tm concentrations, the heat capacity cannot be accounted for by randomly distributed strains, demonstrating the presence of significant strain correlations between sites. For intermediate Tm concentrations, these correlations must still exist, but the data cannot be distinguished from that which would be obtained from a 2D Gaussian distribution. The cross-over between these limits is discussed in terms of the interplay of key length scales in the substituted material. The central result of this work, that local strains arising from chemical substitution are not uncorrelated, has implications for the range of validity of theoretical models based on random effective fields that are used to describe such chemically substituted materials, particularly when electronic nematic correlations are present.

Because Full Waveform Inversion (FWI) works with a massive amount of data, its execution requires much time and computational resources, being restricted to large-scale computer systems such as supercomputers. Techniques such as FWI adapt well to parallel computing and can be parallelized in shared memory systems using the application programming interface (API) OpenMP. The management of parallel tasks can be performed through loop schedulers contained in OpenMP. The dynamic scheduler stands out for distributing predefined fixed-size chunk sizes to idle processing cores at runtime. It can better adapt to FWI, where data processing can be irregular. However, the relationship between the size of the chunk size and the runtime is unknown. Optimization techniques can employ meta-heuristics to explore the parameter search space, avoiding testing all possible solutions. Here, we propose a strategy to use the Parameter Auto Tuning for Shared Memory Algorithms (PATSMA), with Coupled Simulated Annealing (CSA) as its optimization method, to automatically adjust the chunk size for the dynamic scheduling of wave propagation, one of the most expensive steps in FWI. Since testing each candidate chunk size in the complete FWI is unpractical, our approach consists of running a PATSMA where the objective function is the runtime of the first time iteration of the first seismic shot of the first FWI iteration. The resulting chunk size is then employed in all wave propagations involved in an FWI. We conducted tests to measure the runtime of an FWI using the proposed autotuning, varying the problem size and running on different computational environments, such as supercomputers and cloud computing instances. The results show that applying the proposed autotuning in an FWI reduces its runtime by up to 70.46% compared to standard OpenMP schedulers.

Program synthesis with Genetic Programming searches for a correct program that satisfies the input specification, which is usually provided as input-output examples. One particular challenge is how to effectively handle loops and recursion avoiding programs that never terminate. A helpful abstraction that can alleviate this problem is the employment of Recursion Schemes that generalize the combination of data production and consumption. Recursion Schemes are very powerful as they allow the construction of programs that can summarize data, create sequences, and perform advanced calculations. The main advantage of writing a program using Recursion Schemes is that the programs are composed of well defined templates with only a few parts that need to be synthesized. In this paper we make an initial study of the benefits of using program synthesis with fold and unfold templates, and outline some preliminary experimental results. To highlight the advantages and disadvantages of this approach, we manually solved the entire GPSB benchmark using recursion schemes, highlighting the parts that should be evolved compared to alternative implementations. We noticed that, once the choice of which recursion scheme is made, the synthesis process can be simplified as each of the missing parts of the template are reduced to simpler functions, which are further constrained by their own input and output types.

Fact-checking-specific search tools such as Google Fact Check are a promising way to combat misinformation on social media, especially during events bringing significant social influence, such as the COVID-19 pandemic and the U.S. presidential elections. However, the usability of such an approach has not been thoroughly studied. We evaluated the performance of Google Fact Check by analyzing the retrieved fact-checking results regarding 1,000 COVID-19-related false claims and found it able to retrieve the fact-checking results for 15.8% of the input claims, and the rendered results are relatively reliable. We also found that the false claims receiving different fact-checking verdicts (i.e., "False," "Partly False," "True," and "Unratable") tend to reflect diverse emotional tones, and fact-checking sources tend to check the claims in different lengths and using dictionary words to various extents. Claim variations addressing the same issue yet described differently are likely to retrieve distinct fact-checking results. We suggest that the quantities of the retrieved fact-checking results could be optimized and that slightly adjusting input wording may be the best practice for users to retrieve more useful information. This study aims to contribute to the understanding of state-of-the-art fact-checking tools and information integrity.

The electronic spectra of altermagnets are a fertile ground for non-trivial topology due to the unique interplay between time-reversal and crystalline symmetries. This is reflected in the unconventional Zeeman splitting between bands of opposite spins, which emerges in the absence of spin-orbit coupling (SOC) and displays nodes along high-symmetry directions. Here, we argue that even for a small SOC, the direction of the magnetic moments in the altermagnetic state has a profound impact on the electronic spectrum, enabling novel topological phenomena to appear. By investigating microscopic models for two-dimensional (2D) and three-dimensional (3D) altermagnets, motivated in part by rutile materials, we demonstrate the emergence of hitherto unexplored Dirac crossings between bands of same spin but opposite sublattices. The direction of the moments determines the fate of these crossings when the SOC is turned on. We focus on the case of out-of-plane moments, which forbid an anomalous Hall effect and thus ensure that no weak magnetization is triggered in the altermagnetic state. In 2D, the SOC gaps out the Dirac crossings, resulting in mirror Chern bands that enable the quantum spin Hall effect and undergo a topological transition to trivial bands upon increasing the magnitude of the magnetic moment. On the other hand, in 3D the crossings persist even in the presence of SOC, forming Weyl nodal loops protected by mirror symmetry. Finally, we discuss possible ways to control these effects in altermagnetic material candidates., Comment: 7 pages, 4 figures; Suppl.: 22 pages, 3 figures

We investigate the influence of crystallographic twins on the elastoplastic response of $\gamma$-TiAl intermetallics via full-field FFT-based computations. We first introduce a hierarchical stochastic model, which is used to simulate synthetic polycrystalline microstructures containing twin grains with certain morphologies, and apply it to generate representative volume elements. Second, we develop a Fourier-based method with regularization for solving the effective and local mechanical response of polycrystalline media using the M\'eric-Cailletaud crystal plasticity constitutive law. Numerical results show that, across configurations of twinning, the corresponding average effective response is similar. Although differences were quantified, the effect of twins on the yield stress is negligible in practice (less than $1\%$).

In this article, we show the H\"older invariance of the Henry-Parusinski invariant. For a single germ $ f$, the Henry-Parusinski invariant of $ f $ is given in terms of the leading coefficients of the asymptotic expansion of $ f $ along the branches of the generic polar curve of $f$. As a consequence, we obtain that the classification problem of polynomial function-germs, with uniformly bounded degree, under H\"older equivalence, admits continuous moduli., Comment: 19 pages

The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model's inherent uncertainty, especially when dealing with sensitive decision-making domains, such as finance or healthcare. Given that model-predicted scores are commonly seen as event probabilities, calibration is crucial for accurate interpretation. In our study, we analyze the sensitivity of various calibration measures to score distortions and introduce a refined metric, the Local Calibration Score. Comparing recalibration methods, we advocate for local regressions, emphasizing their dual role as effective recalibration tools and facilitators of smoother visualizations. We apply these findings in a real-world scenario using Random Forest classifier and regressor to predict credit default while simultaneously measuring calibration during performance optimization.

Pairwise Markov Models (PMMs) extend the wellknown Hidden Markov Models (HMMs). Being significantly more general, PMMs enable several types of processing, like Bayesian filtering or smoothing, similar to those used in HMMs. In this paper, we deal with Bayesian forecasting. The aim is to show analytically in the simple stationary Gaussian case that the extent results obtained with HMM can be improved. We complete contributions with a theoretical error study and two real examples we deal with. Experiments show that PMMs-based forecasting can significantly improve HMMs-based ones.

Detection of anomalous situations for complex mission-critical systems holds paramount importance when their service continuity needs to be ensured. A major challenge in detecting anomalies from the operational data arises due to the imbalanced class distribution problem since the anomalies are supposed to be rare events. This paper evaluates a diverse array of machine learning-based anomaly detection algorithms through a comprehensive benchmark study. The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical machine learning including various tree-based approaches to deep learning and outlier detection methods. The inclusion of 104 publicly available and a few proprietary industrial systems datasets enhances the diversity of the study, allowing for a more realistic evaluation of algorithm performance and emphasizing the importance of adaptability to real-world scenarios. The paper dispels the deep learning myth, demonstrating that though powerful, deep learning is not a universal solution in this case. We observed that recently proposed tree-based evolutionary algorithms outperform in many scenarios. We noticed that tree-based approaches catch a singleton anomaly in a dataset where deep learning methods fail. On the other hand, classical SVM performs the best on datasets with more than 10% anomalies, implying that such scenarios can be best modeled as a classification problem rather than anomaly detection. To our knowledge, such a study on a large number of state-of-the-art algorithms using diverse data sets, with the objective of guiding researchers and practitioners in making informed algorithmic choices, has not been attempted earlier.

Social networks are vital for information sharing, especially in the health sector for discussing diseases and treatments. These platforms, however, often feature posts as brief texts, posing challenges for Artificial Intelligence (AI) in understanding context. We introduce a novel hybrid approach combining community-maintained knowledge graphs (like Wikidata) with deep learning to enhance the categorization of social media posts. This method uses advanced entity recognizers and linkers (like Falcon 2.0) to connect short post entities to knowledge graphs. Knowledge graph embeddings (KGEs) and contextualized word embeddings (like BERT) are then employed to create rich, context-based representations of these posts. Our focus is on the health domain, particularly in identifying posts related to eating disorders (e.g., anorexia, bulimia) to aid healthcare providers in early diagnosis. We tested our approach on a dataset of 2,000 tweets about eating disorders, finding that merging word embeddings with knowledge graph information enhances the predictive models' reliability. This methodology aims to assist health experts in spotting patterns indicative of mental disorders, thereby improving early detection and accurate diagnosis for personalized medicine.

This paper investigates the tracking problem of a smooth coordinate-invariant trajectory using dual quaternion algebra. The proposed architecture consists of a cascade structure in which the outer-loop MPC performs real-time smoothing of the manipulator's end-effector twist while an inner-loop kinematic controller ensures tracking of the instantaneous desired end-effector pose. Experiments on a $7$-DoF Franka Emika Panda robotic manipulator validate the proposed method demonstrating its application to constraint the robot twists, accelerations and jerks within prescribed bounds.

We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81 % of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.

Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contemporary predictive models. While enhancing prognostic performance, geospatial data also has the potential to perpetuate many historical socio-economic patterns, raising concerns about a resurgence of biases and exclusionary practices, with their disproportionate impacts on society. Addressing this, our paper emphasizes the crucial need to identify and rectify such biases and calibration errors in predictive models, particularly as algorithms become more intricate and less interpretable. The increasing granularity of geospatial information further introduces ethical concerns, as choosing different geographical scales may exacerbate disparities akin to redlining and exclusionary zoning. To address these issues, we propose a toolkit for identifying and mitigating biases arising from geospatial data. Extending classical fairness definitions, we incorporate an ordinal regression case with spatial attributes, deviating from the binary classification focus. This extension allows us to gauge disparities stemming from data aggregation levels and advocates for a less interfering correction approach. Illustrating our methodology using a Parisian real estate dataset, we showcase practical applications and scrutinize the implications of choosing geographical aggregation levels for fairness and calibration measures.

At the 3 GeV ring of the MAX IV Laboratory, a fourth generation ring-based synchrotron light source, an asymmetric influence of the sign of the amplitude-dependent tune shift (ADTS) on the transverse mode-coupling instability (TMCI) has been observed. Measurements of the instability, in dedicated single-bunch experiments at low chromaticity, revealed a significant dependence of the dynamics of the instability above threshold on the sign of the ADTS. While for a negative sign of the ADTS the crossing of the instability does not lead to a loss of beam current, a positive sign results in the loss of 40% or more of the beam current at the threshold. In order to investigate the observed asymmetry, systematic measurement of beam dynamics above the threshold have been conducted in combination with particle tracking simulations with mbtrack2 and theoretical calculations of the Landau damping due to the ADTS. The findings point towards an influence of the Landau damping in combination with the low synchrotron frequency, which indicates that this effect could become relevant in future low-emittance electron storage rings., Comment: 13 pages, 15 figures

The normal modes of Proca field perturbations in $d$-dimensional anti-de Sitter spacetime, AdS$_d$ for short, with reflective Dirichlet boundary conditions, are obtained exactly. Within the Ishibashi-Kodama framework, we decompose the Proca field in scalar-type and vector-type components, according to their tensorial behavior on the $(d-2)$-sphere $\mathcal{S}^{d-2}$. Two of the degrees of freedom of the Proca field are described by scalar-type components, which in general are coupled due to the mass of the field, but in AdS$_d$ we show that they can be decoupled. The other $d-3$ degrees of freedom of the field are described by a vector-type component that generically decouples completely. The normal modes and their frequencies for both the scalar-type and vector-type components of the Proca field are then obtained analytically. Additionally, we analyze the normal modes of the Maxwell field as the massless limit of the Proca field. We find that for scalar-type perturbations in $d=4$ there is a discontinuity in the massless limit, in $d=5$ the massless limit is well defined using Dirichlet-Neumann rather than Dirichlet boundary conditions, and in $d>5$ the massless limit is completely well defined, i.e., it is obtained smoothly from the massless limit of the scalar-type perturbations of the Proca field. For vector-type perturbations the Maxwell field limit is obtained smoothly for all $d$ from the massless limit of the vector-type perturbations of the Proca field., Comment: 16 pages

Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic Algorithms (GAs). It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. The method uses as initial population size an estimation of the minimum size needed to supply enough building blocks, using a fixed-size selectorecombinative GA converging within some confidence interval toward good solutions for a particular problem. Following this methodology, a scalability analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap functions in order to assess whether SVPS-GA improves performances compared to a fixed-size GA under different problem instances and difficulty levels. Results show several combinations of speed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.

In this paper we consider the monoids of all partial endomorphisms, of all partial weak endomorphisms, of all injective partial endomorphisms, of all partial strong endomorphisms and of all partial strong weak endomorphisms of a star graph with a finite number of vertices. Our main objective is to determine their ranks. We also describe their Green's relations, calculate their cardinalities and study their regularity.

We introduce a new concept in the Absorbing Ideal Theory in commutative rings, that is, the $\omega$-stable groups. We will provide examples and non-examples of these groups, and establish their relationship with H-congruence. Ultimately, we will study the action of these groups considering the transitivity condition, Comment: 9 pages

We test the predictions of hadronic interaction models regarding the depth of maximum of air-shower profiles, $X_{max}$, and ground-particle signals in water-Cherenkov detectors at 1000 m from the shower core, $S(1000)$, using the data from the fluorescence and surface detectors of the Pierre Auger Observatory. The test consists in fitting the measured two-dimensional ($S(1000)$, $X_{max}$) distributions using templates for simulated air showers produced with hadronic interaction models EPOS-LHC, QGSJet II-04, Sibyll 2.3d and leaving the scales of predicted $X_{max}$ and the signals from hadronic component at ground as free fit parameters. The method relies on the assumption that the mass composition remains the same at all zenith angles, while the longitudinal shower development and attenuation of ground signal depend on the mass composition in a correlated way. The analysis was applied to 2239 events detected by both the fluorescence and surface detectors of the Pierre Auger Observatory with energies between $10^{18.5}$ to $10^{19.0}$ eV and zenith angles below $60^\circ$. We found, that within the assumptions of the method, the best description of the data is achieved if the predictions of the hadronic interaction models are shifted to deeper $X_{max}$ values and larger hadronic signals at all zenith angles. Given the magnitude of the shifts and the data sample size, the statistical significance of the improvement of data description using the modifications considered in the paper is larger than $5\sigma$ even for any linear combination of experimental systematic uncertainties., Comment: Published version

A stochastic 3D microstructure model for polycrystals is introduced which incorporates two types of twin grains, namely neighboring and inclusion twins. They mimic the presence of crystal twins in $\gamma$-TiAl polycrystalline microstructures as observed by 3D imaging techniques. The polycrystal grain morphology is modeled by means of Voronoi and -- more generally -- Laguerre tessellations. The crystallographic orientation of each grain is either sampled uniformly on the space of orientations or chosen to be in a twinning relation with another grain. The model is used to quantitatively study relationships between morphology and mechanical properties of polycrystalline materials. For this purpose, full-field Fourier-based computations are performed to investigate the combined effect of grain morphology and twinning on the overall elastic response. For $\gamma$-TiAl polycrystallines, the presence of twins is associated with a softer response compared to polycrystalline aggregates without twins. However, when comparing the influence on the elastic response, a statistically different polycrystalline morphology has a much smaller effect than the presence of twin grains. Notably, the bulk modulus is almost insensitive to the grain morphology and exhibits much less sensitivity to the presence of twins compared to the shear modulus. The numerical results are consistent with a two-scale homogenization estimate that utilizes laminate materials to model the interactions of twins.

Programs with high levels of complexity often face challenges in adjusting execution parameters, particularly when these parameters vary based on the execution context. These dynamic parameters significantly impact the program's performance, such as loop granularity, which can vary depending on factors like the execution environment, program input, or the choice of compiler. Given the expensive nature of testing each case individually, one viable solution is to automate parameter adjustments using optimization methods. This article introduces PATSMA, a parameter auto-tuning tool that leverages Coupled Simulated Annealing (CSA) and Nelder-Mead (NM) optimization methods to fine-tune existing parameters in an application. We demonstrate how auto-tuning can contribute to the real-time optimization of parallel algorithms designed for shared memory systems. PATSMA is a C++ library readily available under the MIT license.

We evaluated all two loop conformal integrals appearing in five point correlation functions of protected operators of $\mathcal{N} = 4$ Super Yang-Mills in several kinematical regimes. Starting from the correlation function of the lightest operators of the theory, we were able to extract structure constants of up to two spinning operators for small and large values of polarizations and spin. We conjectured an universal all loop behaviour for the large spin small polarization structure constants and comment on the subtleties of analytically continuing it from finite to large spin. We also consider correlation functions of heavier operators that get factorized in the more fundamental object called decagon. We fixed this object at two loops in general kinematics and studied its physical properties under OPE and null limits., Comment: 34 pages, 2 figures, 6 auxiliary files

Supercomputers have revolutionized how industries and scientific fields process large amounts of data. These machines group hundreds or thousands of computing nodes working together to execute time-consuming programs that require a large amount of computational resources. Over the years, supercomputers have expanded to include new and different technologies characterizing them as heterogeneous. However, executing a program in a heterogeneous environment requires attention to a specific aspect of performance degradation: load imbalance. In this research, we address the challenges associated with load imbalance when scheduling many homogeneous tasks in a heterogeneous environment. To address this issue, we introduce the concept of adaptive asynchronous work-stealing. This approach collects information about the nodes and utilizes it to improve work-stealing aspects, such as victim selection and task offloading. Additionally, the proposed approach eliminates the need for extra threads to communicate information, thereby reducing overhead when implementing a fully asynchronous approach. Our experimental results demonstrate a performance improvement of approximately 10.1\% compared to other conventional and state-of-the-art implementations., Comment: 32 pages, 5 figures

Following a recent proposal to describe inelastic eikonal scattering processes in terms of gravitationally dressed elastic eikonal amplitudes, we motivate a collinear double graviton dressing and investigate its properties. This is derived from a generalized Wilson line operator in the worldline formalism by integrating over fluctuations of the eikonal trajectories of external particles in gravitationally interacting theories. The dressing can be expressed as a product of exponential terms -- a coherent piece with contributions to all odd orders in the gravitational coupling constant and a term quadratic in graviton modes, with the former providing classical gravitational wave observables. In particular, the coherent dressing involves $\mathcal{O}(\kappa^3)$ subleading double graviton corrections to the Weinberg soft factor. We use this dressing to derive expressions for the waveform, radiative momentum spectrum and angular momentum. In a limiting case of the waveform, we derive the nonlinear memory effect resulting from the emission of nearly soft gravitons from a scattering process., Comment: Matches published version; 49 pages, 1 figure, revisions of Sec. 2.3, Sec. 4.1 and Sec. 5 with broader literature review, minor revision of notation and typos corrected

We extend the Abreu-Guillemin theory of invariant K\"ahler metrics from toric symplectic manifolds to any symplectic manifold admitting a toric action of a symplectic torus bundle. We show that these are precisely the symplectic manifolds admitting a Lagrangian fibration with elliptic singularities. The base of such a toric Lagrangian fibration is a codimension 0 submanifold with corners of an integral affine manifold, called a Delzant subspace. This concept generalizes the Delzant polytope associated with a compact symplectic toric manifold. Given a Delzant subspace of finite type, we provide a Delzant-type construction of a Lagrangian fibration with the moment image being the specified Delzant subspace. We establish a 1:1 correspondence between invariant K\"ahler metrics and a pair consisting of an elliptic connection on the total space of the fibration and a hybrid $b$-metric on the base Delzant subspace, both with specified residues over the facets. Finally, we characterize extremal invariant K\"ahler metrics as those whose scalar curvature descends to an affine function on the base integral affine manifold. We show that this provides a method for finding and constructing extremal K\"ahler metrics., Comment: 58 pages. Comments welcomed!

Motivated by our recent findings in [Phys. Rev. Lett. 128, 013902, 2022], which introduces a new class of electromagnetic bulk materials whose response is similar to conventional semiconductor transistors, here we propose a one-dimensional (1D) version of such a material based on transmission lines coupled with FET isolators. We demonstrate that the response of this 1D system is nonreciprocal and non-Hermitian, analogous to the idealized transistor-metamaterial, and is also characterized by a broken time-reversal symmetry. We analyze the wave propagation in the system and find that the interaction between the eigenmodes can either lead to gain or loss depending on the propagation distance. Furthermore, it is also shown that the system may be operated at an exceptional point, wherein the response of the structure is singular, and the power gain is maximized. Finally, we demonstrate that the exceptional point coincides with the point of operation of typical microwave amplifiers, such as the distributed amplifier.

Let $\Omega_n$ be a finite chain with $n$ elements $(n\in\mathbb{N})$, and let $\mathcal{POPI}_{n}$ be the semigroup of all injective orientation-preserving partial transformations of $\Omega_n$. In this paper, for any nonempty subset $Y$ of $\Omega_n$, we consider the subsemigroup $\mathcal{POPI}_{n}(Y)$ of $\mathcal{POPI}_{n}$ of all transformations with range contained in $Y$. We describe the Green's relations and study the regularity of $\mathcal{POPI}_{n}(Y)$. Moreover, we calculate the rank of $\mathcal{POPI}_{n}(Y)$ and determine when two semigroups of this type are isomorphic.

The crystallographic restriction theorem constrains two-dimensional nematicity to display either Ising ($Z_{2}$) or three-state-Potts ($Z_{3}$) critical behaviors, both of which are dominated by amplitude fluctuations. Here, we use group theory and microscopic modeling to show that this constraint is circumvented in a $30^{\circ}$-twisted hexagonal bilayer due to its emergent quasicrystalline symmetries. We find a critical phase dominated by phase fluctuations of a $Z_{6}$ nematic order parameter and bounded by two Berezinskii-Kosterlitz-Thouless (BKT) transitions, which displays only quasi-long-range nematic order. The electronic spectrum in the critical phase displays a thermal pseudogap-like behavior, whose properties depend on the anomalous critical exponent. We also show that an out-of-plane magnetic field induces nematic phase fluctuations that suppress the two BKT transitions via a mechanism analogous to the Hall viscoelastic response of the lattice, giving rise to a putative nematic quantum critical point with emergent continuous symmetry. Finally, we demonstrate that even in the case of an untwisted bilayer, a critical phase emerges when the nematic order parameter changes sign between the two layers, establishing an odd-parity nematic state., Comment: Manuscript (7 pages, 3 figures) plus Supplementary Material (5 pages, 1 figure)

The goal of this article is to give an overview of the current limitations and epistemological barriers in Science and Scientific Philosophy from a very general point of view. We first list and define the types of knowledge nous, doxa and episteme, and the Subject-Observer and Object(s) of study, to proceed showing the different types of barriers that difficult the knowledge of the physical world: limitations in the language, in the logic of the Subject-Observer. Later, we discriminate between technological barriers, (temporary) limits and absolute epistemic barriers. The last type of limits are presented and discussed in some detail: the quantum of action, Planck's scale and quantum gravity (showing the importance of the trans-Planckian scale for structure formation), the cosmological horizon (a limit to the present observable Universe) and the event horizons (disconnecting the inside of some spacetimes from the rest of the Universe). We argue that physical problems in which absolute barriers seem to determine the end of the attainable knowledge, are in fact amenable to be studied, at least indirectly., Comment: 19 pp. 4 figures. Accepted for publication in Cosmos and History

We prove that in any $n$-vertex complete graph there is a collection $\mathcal{P}$ of $(1 + o(1))n$ paths that strongly separates any pair of distinct edges $e, f$, meaning that there is a path in $\mathcal{P}$ which contains $e$ but not $f$. Furthermore, for certain classes of $n$-vertex $\alpha n$-regular graphs we find a collection of $(\sqrt{3 \alpha + 1} - 1 + o(1))n$ paths that strongly separates any pair of edges. Both results are best-possible up to the $o(1)$ term.

Carbon dioxide (CO2) trapping in capillary networks of reservoir rocks is a pathway to long-term geological storage. At pore scale, the CO2 trapping potential depends on injection pressure, temperature, and the rock's interaction with the surrounding fluids. Modeling this interaction requires adequate representations of both capillary volume and surface. For the lack of scalable representations, however, the prediction of a rock's CO2 storage potential has been challenging. Here, we report how to represent a rock's pore space by statistically sampled capillary networks (ssCN) that preserve morphological rock characteristics. We have used the ssCN method to simulate CO2 drainage within a representative sandstone sample at reservoir pressures and temperatures, exploring intermediate- and CO2-wet conditions. This wetting regime is often neglected, despite evidence of plausibility. By raising pressure and temperature we observe increasing CO2 penetration within the capillary network. For contact angles approaching 90 degrees, the CO2 saturation exhibits a pronounced maximum reaching 80 percent of the accessible pore volume. This is about twice as high as the saturation values reported previously. For enabling validation of our results and a broader application of our methodology, we have made available the rock tomography data, the digital rock computational workflows, and the ssCN models used in this study., Comment: Main article: 16 pages and 3 figures. Supplementary Information: 17 pages, 3 equations, 13 figures and 3 tables. Includes DOI for accompanying digital rock data, URL of github repository for flow simulator code, and of accompanying digital rock processing python code and automation of the scientific workflows

End-to-end self-supervised models have been proposed for estimating the success of future candidate grasps and video predictive models for generating future observations. However, none have yet studied these two strategies side-by-side for addressing the aforementioned grasping problem. We investigate and compare a model-free approach, to estimate the success of a candidate grasp, against a model-based alternative that exploits a self-supervised learnt predictive model that generates a future observation of the gripper about to grasp an object. Our experiments demonstrate that despite the end-to-end model-free model obtaining a best accuracy of 72%, the proposed model-based pipeline yields a significantly higher accuracy of 82%.

In general relativity, all vacuum black holes are described by the Kerr solution. Beyond general relativity, there is a prevailing expectation that deviations from the Kerr solution increase with the horizon curvature. We challenge this expectation by showing that, in a scalar-Gauss-Bonnet theory, black holes scalarize in a finite, adjustable window of black-hole masses, bounded from above and below. In this theory, there is an interplay between curvature scales and compactness, which we expect to protect neutron stars and other less compact objects from scalarization. In addition, this theory is the first to avoid the catastrophic instability of early-universe cosmology that affects previous scalarization models. In this theory, black-hole uniqueness can be broken at supermassive black-hole scales, while stellar-mass black holes remain well-described by the Kerr solution. To probe this scenario, observations targeting supermassive black holes are necessary., Comment: 6 pages, 3 figures + supplemental material; V2: Included a discussion on the cosmological stability of the theory + minor changes

Deep Learning models have achieved remarkable performance in tasks such as image classification or generation, often surpassing human accuracy. However, they can struggle to learn new tasks and update their knowledge without access to previous data, leading to a significant loss of accuracy known as Catastrophic Forgetting (CF). This phenomenon was first observed by McCloskey and Cohen in 1989 and remains an active research topic. Incremental learning without forgetting is widely recognized as a crucial aspect in building better AI systems, as it allows models to adapt to new tasks without losing the ability to perform previously learned ones. This article surveys recent studies that tackle CF in modern Deep Learning models that use gradient descent as their learning algorithm. Although several solutions have been proposed, a definitive solution or consensus on assessing CF is yet to be established. The article provides a comprehensive review of recent solutions, proposes a taxonomy to organize them, and identifies research gaps in this area.

Skyrmions are spin-swirling textures hosting wonderful properties with potential implications in information technology. Such magnetic particles carry a magnetization, whose amplitude is crucial to establish them as robust magnetic bits, while their topological nature gives rise to a plethora of exquisite features such as topological protection, the skyrmion and topological Hall effects as well as the topological orbital moment. These effects are all induced by an emergent magnetic field directly proportional to the three-spin scalar chirality, $\chi= (\mathbf{S}_i\times\mathbf{S}_j)\cdot \mathbf{S}_k$, and shaped by the peculiar spatial dependence of the magnetization. Here, we demonstrate the existence of novel chiral magnetizations emerging from the interplay of spin-orbit interaction and either $\chi$ or the two-spin vector chirality $\boldsymbol{\kappa} = \mathbf{S}_i\times\mathbf{S}_j$. By scrutinizing correlations among the spin, orbital (trivial and chiral) magnetizations, we unveil from ab-initio universal patterns, quantify the rich set of magnetizations carried by single skyrmions generated in PdFe bilayer on Ir(111) surface and demonstrate the ability to engineer their magnitude via controlled implantation of impurities. We anticipate that our findings can guide the design of disruptive storage devices based on skyrmionic bits by encoding the desired magnetization with strategic seeding of defects., Comment: 23 pages, 4 figures